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Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: This letter proposes a band-dependent variable step-size sign subband adaptive filter using the concept of mean-square deviation (MSD) minimization, which performs better than the existing algorithms in aspects of the convergence rate and the steady-state estimation error.

38 citations

Dissertation
04 Apr 2003
TL;DR: The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter, and presents six approaches for selecting MWF rank and an overall design space taxonomy.
Abstract: Robust Implementations of the Multistage Wiener Filter By John David Hiemstra The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition produces a reduced rank filter with many benefits, for example, improved performance. This dissertation extends knowledge of the MWF in four areas. The first area is rank and sample support compression. This dissertation examines, under a wide variety of conditions, the size of the adaptive subspace required by the MWF (i.e., the rank) as well as the required number of training samples. Comparisons are made with other algorithms such as the eigenvector-based principal components algorithm. The second area investigated in this dissertation concerns “soft stops”, i.e., the insertion of diagonal loading into the MWF. Several methods for inserting loading into the MWF are described, as well as methods for choosing the amount of loading. The next area investigated is MWF rank selection. The MWF will outperform the classical Wiener filter when the rank is properly chosen. This dissertation presents six approaches for selecting MWF rank. The algorithms are compared to one another and an overall design space taxonomy is presented. Finally, as digital modelling capabilities become more sophisticated there is emerging interest in augmenting adaptive processing algorithms to incorporate prior knowledge. This dissertation presents two methods for augmenting the MWF, one based on linear constraints and a second based on non-zero weight vector initialization. Both approaches are evaluated under ideal and perturbed conditions. Together the research described in this dissertation increases the utility and robustness of the multistage Wiener filter. The analysis is presented in the context of adaptive array processing, both spatial array processing and space-time adaptive processing for airborne radar. The results, however, are applicable across the entire spectrum of adaptive signal processing applications.

37 citations

Patent
03 Aug 2010
TL;DR: In this article, a blind adaptive filter for narrowband interference cancellation is proposed, which includes an adaptive filter, a delay unit coupled to the adaptive filter to generate a delayed signal with a predetermined delay length.
Abstract: The present invention relates to a blind adaptive filter for narrowband interference cancellation, which includes an adaptive filter, a delay unit coupled to the adaptive filter for generating a delayed signal with a predetermined delay length from the output signal of the adaptive filter, and an error calculation unit coupled to the adaptive filter and the delay unit. The error calculation unit compares the output signal from the adaptive filter and the delayed signal from the delay unit to extract error information, and feedback the first error information to the adaptive filter. The first error information is formed of a transfer function including a number of coefficients, and used to adjust the adaptive filter and remove interference in the next input signal. The disclosed technique is also applicable in wideband receivers, as well as resisting multiple strong narrowband interferences having a frequency sweep rate of tens of milliseconds.

37 citations

Journal ArticleDOI
TL;DR: An evolutionary single Gabor kernel (ESGK) based filter approach is proposed for face recognition and a new eigen value based classifier is introduced, which outperforms the state-of-the-art methods.

37 citations

Journal ArticleDOI
TL;DR: This paper considers the problem of optimizing spatial frequency domain filters for detecting a class of edges in images and shows that the optimal filter represents the Laplacian operator in image space followed by a low pass filter with a cutoff frequency.
Abstract: Edge detection and enhancement are required in a number of important image processing applications. In this paper we consider the problem of optimizing spatial frequency domain filters for detecting a class of edges in images. The filter is optimum in that it produces maximum energy in the vicinity of the location of the edge for a given spatial resolution I and the bandwidth Ω. We show that the filter transfer function can be specified in terms of the prolate spheroidal wavefunctions for a given space–bandwidth product IΩ. Further we show that for values of IΩ less than 2, the optimal filter represents the Laplacian operator in image space followed by a low pass filter with a cutoff frequency Ω.

37 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202322
202251
202113
202020
201931
201844